Semantic3D - Results

	Name	A_IoU	OA	[s]	IoU 1	IoU 2	IoU 3	IoU 4	IoU 5	IoU 6	IoU 7	IoU 8
1	CRFConv_big	0.749	0.942	0.10	0.981	0.911	0.818	0.448	0.952	0.408	0.594	0.880
Anonymous submission
2	DenseKPNet	0.779	0.949	1.00	0.979	0.927	0.888	0.512	0.950	0.450	0.709	0.816
Anonymous submission
3	LCDE-Net	0.786	0.950	100.00	0.978	0.940	0.851	0.518	0.952	0.443	0.739	0.866
Anonymous submission
4	CRFConv	0.723	0.941	0.10	0.977	0.923	0.848	0.466	0.950	0.400	0.405	0.812
Anonymous submission
5	shao477	0.748	0.939	1.00	0.977	0.879	0.845	0.437	0.951	0.500	0.634	0.764
Anonymous submission
6	AGMMConv	0.761	0.950	0.10	0.977	0.939	0.839	0.500	0.958	0.498	0.529	0.848
Adaptive GMM Convolution for Point Cloud Learning (BMVC2021)
7	FFA-Net	0.764	0.949	158.50	0.977	0.946	0.854	0.503	0.952	0.454	0.612	0.814
Anonymous submission
8	IGDC	0.783	0.951	2.00	0.977	0.940	0.879	0.526	0.953	0.467	0.722	0.799
Anonymous submission
9	AnchorConv_AR	0.777	0.949	111.00	0.976	0.930	0.857	0.531	0.951	0.421	0.721	0.827
Anonymous submission
10	SPC	0.752	0.943	100.00	0.976	0.931	0.836	0.484	0.947	0.429	0.586	0.826
Anonymous submission
11	shao377	0.780	0.951	1.00	0.976	0.925	0.873	0.514	0.961	0.526	0.651	0.814
Anonymous submission
12	AIC-Net	0.780	0.951	383.30	0.976	0.925	0.873	0.514	0.961	0.526	0.651	0.814
Anonymous submission
13	EU_IMP	0.796	0.955	1.00	0.976	0.944	0.912	0.628	0.960	0.504	0.676	0.767
Anonymous submission
14	LGS-Net	0.790	0.953	432.00	0.976	0.946	0.857	0.536	0.960	0.519	0.730	0.800
Anonymous submission
15	SPNet	0.770	0.945	1300.00	0.975	0.900	0.892	0.511	0.951	0.421	0.727	0.786
Anonymous submission
16	10GRU	0.673	0.911	10800.00	0.975	0.769	0.840	0.319	0.935	0.262	0.587	0.700
Anonymous submission
17	c3	0.778	0.946	600.00	0.975	0.949	0.870	0.549	0.942	0.428	0.720	0.788
Anonymous submission
18	RGNet	0.747	0.945	1.00	0.975	0.930	0.881	0.481	0.946	0.362	0.720	0.680
Fast Point Cloud Registration Using Semantic Segmentation - DICTA 2019
19	SPGraph	0.732	0.940	3000.00	0.974	0.926	0.879	0.440	0.932	0.310	0.635	0.762
Large-scale Point cloud segmentation with superpoint graphs, Loic Landrieu and Martin Simonovsky, CVPR2018
20	BRNet_0910_2	0.777	0.950	1.00	0.973	0.941	0.877	0.522	0.955	0.482	0.677	0.790
Anonymous submission
21	shell_v2	0.685	0.932	3000.00	0.973	0.910	0.837	0.397	0.931	0.271	0.448	0.715
Anonymous submission
22	AI_Lab-NIE	0.792	0.955	1.00	0.972	0.942	0.890	0.572	0.964	0.503	0.713	0.777
Anonymous submission
23	XPCONVP	0.778	0.946	111.00	0.971	0.911	0.879	0.532	0.948	0.415	0.757	0.811
Anonymous submission
24	PCSNet	0.712	0.943	1500.00	0.971	0.950	0.879	0.525	0.941	0.388	0.355	0.687

25	TBDV2	0.800	0.954	1.00	0.971	0.935	0.868	0.551	0.959	0.531	0.753	0.829
Anonymous submission
26	shao473	0.786	0.952	1.00	0.971	0.934	0.872	0.533	0.957	0.526	0.691	0.804
Anonymous submission
27	SCF-Net	0.776	0.947	563.60	0.971	0.918	0.863	0.512	0.953	0.505	0.679	0.807
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation (CVPR2021)
28	DG	0.682	0.912	23.84	0.965	0.869	0.818	0.382	0.916	0.287	0.558	0.658
Anonymous submission
29	PAI_Conv	0.764	0.944	1.00	0.964	0.904	0.867	0.496	0.956	0.454	0.690	0.784
Anonymous submission
30	shellnet_v2	0.693	0.932	3000.00	0.963	0.904	0.839	0.410	0.942	0.347	0.439	0.702
@inproceedings{zhang-shellnet-iccv19, title = {ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics}, author = {Zhiyuan Zhang and Binh-Son Hua and Sai-Kit Yeung}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2019} }
31	AutoNN	0.763	0.941	838.60	0.958	0.887	0.841	0.463	0.952	0.455	0.744	0.808
Anonymous submission
32	UDNV	0.773	0.945	259200.00	0.957	0.903	0.844	0.505	0.954	0.459	0.727	0.832
Anonymous submission
33	PyramidPoint	0.773	0.945	1.00	0.957	0.903	0.844	0.505	0.954	0.459	0.727	0.832
https://arxiv.org/abs/2011.08692
34	RandLA-Net	0.774	0.948	1.00	0.956	0.914	0.866	0.515	0.957	0.515	0.698	0.768
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (CVPR 2020, Oral)
35	ds	0.774	0.948	23.00	0.956	0.914	0.866	0.515	0.957	0.515	0.698	0.768

36	DLUT_SR	0.563	0.860	1.00	0.953	0.849	0.548	0.296	0.832	0.192	0.320	0.518
Anonymous submission
37	LU-1	0.706	0.915	18.45	0.952	0.889	0.815	0.362	0.913	0.334	0.602	0.779
surveying and mapping institute
38	PG_Net	0.772	0.944	0.00	0.947	0.888	0.875	0.534	0.953	0.528	0.655	0.793
Kangcheng Liu, and Ben M. Chen. (FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling) https://arxiv.org/abs/2012.09439
39	OctFCNNet	0.648	0.894	1200.00	0.943	0.756	0.786	0.342	0.904	0.257	0.478	0.721
Anonymous submission
40	RFCR	0.778	0.943	1.00	0.942	0.891	0.857	0.544	0.950	0.438	0.762	0.837
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning (CVPR2021)
41	ACNNs	0.613	0.908	1.00	0.940	0.862	0.804	0.185	0.904	0.245	0.290	0.673
Anonymous submission
42	ProNet	0.616	0.909	1.00	0.940	0.858	0.808	0.205	0.908	0.248	0.290	0.673
Anonymous submission
43	mpv7	0.747	0.934	265.00	0.938	0.852	0.855	0.509	0.944	0.400	0.690	0.790
Anonymous submission
44	RSSP	0.647	0.920	359.00	0.916	0.870	0.870	0.525	0.930	0.158	0.320	0.589
Anonymous submission
45	KP-FCNN	0.746	0.929	600.00	0.909	0.822	0.842	0.479	0.949	0.400	0.773	0.797
@article{thomas2019KPConv, Author = {Thomas, Hugues and Qi, Charles R. and Deschaud, Jean-Emmanuel and Marcotegui, Beatriz and Goulette, Fran{c{c}}ois and Guibas, Leonidas J.}, Title = {KPConv: Flexible and Deformable Convolution for Point Clouds}, Journal = {The IEEE International Conference on Computer Vision (ICCV)}, Year = {2019} }
46	sem3drandlanet	0.648	0.882	136.20	0.909	0.642	0.780	0.264	0.915	0.301	0.623	0.750
Anonymous submission
47	OctreeNet_CRF	0.591	0.899	184.84	0.907	0.820	0.824	0.393	0.900	0.109	0.312	0.460
F. Wang, Y. Zhuang, H. Gu, and H. Hu, OctreeNetï¼šA Novel Sparse 3D Convolutional Neural Network for Real-time 3D Outdoor Scene Analysis, submitted to IEEE Transactions on Automation Science and Engineering.
48	TMLC-MSR	0.542	0.862	1800.00	0.898	0.745	0.537	0.268	0.888	0.189	0.364	0.447
Timo Hackel, Jan D. Wegner, Konrad Schindler: Fast semantic segmentation of 3d point clouds with strongly varying density. ISPRS Annals - ISPRS Congress, Prague, 2016
49	FilterNet	0.731	0.930	100.00	0.898	0.832	0.865	0.515	0.951	0.385	0.628	0.778
Anonymous submission
50	MS-RRFSegnet	0.725	0.930	2560.00	0.895	0.836	0.837	0.431	0.960	0.536	0.390	0.917
Anonymous submission
51	PAI-Conv-v2	0.720	0.921	1.00	0.887	0.779	0.856	0.466	0.953	0.437	0.621	0.764
Anonymous submission
52	RF_MSSF	0.627	0.903	1643.75	0.876	0.803	0.818	0.364	0.922	0.241	0.426	0.566
@inproceedings{thomas2018semantic, title={Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods}, author={Thomas, Hugues and Deschaud, Jean-Emmanuel and Marcotegui, Beatriz and Goulette, Francois and Le Gall, Yann}, booktitle={3D Vision (3DV), 2018 International Conference on}, year={2018}, organization={IEEE} }
53	GACNet	0.708	0.919	1380.00	0.864	0.777	0.885	0.606	0.942	0.373	0.435	0.778
Anonymous submission
54	NI_IMP_EU_TEST1	0.556	0.859	2400.00	0.857	0.828	0.778	0.360	0.887	0.228	0.416	0.093
IMP EU SunWei
55	DeePr3SS	0.585	0.889	0.00	0.856	0.832	0.742	0.324	0.897	0.185	0.251	0.592
@misc{1705.03428, Author = {Felix JÃ¤remo Lawin and Martin Danelljan and Patrik Tosteberg and Goutam Bhat and Fahad Shahbaz Khan and Michael Felsberg}, Title = {Deep Projective 3D Semantic Segmentation}, Year = {2017}, Eprint = {arXiv:1705.03428}, }
56	XJTLU	0.635	0.894	5.13	0.854	0.744	0.746	0.319	0.930	0.252	0.415	0.820
Cai Y, Huang H, Wang K, Zhang C, Fan L, Guo F. Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM). Remote Sensing. 2021; 13(7):1367. https://doi.org/10.3390/rs13071367
57	EHNet	0.742	0.921	11.72	0.851	0.755	0.896	0.559	0.955	0.508	0.483	0.925
Semantic Segmentation of Terrestrial Laser Scanning Point Clouds Using Locally Enhanced Image-Based Geometric Representations (IEEE Transactions on Geoscience and Remote Sensing) http://dx.doi.org/10.1109/TGRS.2022.3161982
58	new_net	0.595	0.879	100000.00	0.845	0.709	0.766	0.261	0.914	0.186	0.565	0.514
@inproceedings{contreras2019edge, title={Edge-Convolution Point Net for Semantic Segmentation of Large-Scale Point Clouds}, author={Contreras, Jhonatan and Denzler, Joachim}, booktitle={IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium}, pages={5236--5239}, year={2019}, organization={IEEE} }
59	3D-FCNN-TI	0.582	0.875	774.00	0.840	0.711	0.770	0.318	0.899	0.277	0.252	0.590
L. P. Tchapmi, C. B.Choy, I. Armeni, J. Gwak, S. Savarese, SEGCloud: Semantic Segmentation of 3D Point Clouds, International Conference on 3D Vision (3DV), 2017
60	SEGCloud	0.613	0.881	1881.00	0.839	0.660	0.860	0.405	0.911	0.309	0.275	0.643
L. P. Tchapmi, C. B.Choy, I. Armeni, J. Gwak, S. Savarese, SEGCloud: Semantic Segmentation of 3D Point Clouds, International Conference on 3D Vision (3DV), 2017
61	DeepNet	0.437	0.772	64800.00	0.838	0.385	0.548	0.085	0.841	0.151	0.223	0.423
Anonymous submission
62	ThickSeg3D	0.525	0.864	228.01	0.837	0.738	0.553	0.260	0.905	0.141	0.256	0.510
Anonymous submission
63	MSDeepVoxNet	0.653	0.884	115000.00	0.830	0.672	0.838	0.367	0.924	0.313	0.500	0.782
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network, Xavier Roynard, Jean-Emmanuel Deschaud and FranÃ§ois Goulette
64	DeepVoxNet	0.571	0.848	100000.00	0.827	0.531	0.838	0.287	0.899	0.236	0.298	0.650
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network, Xavier Roynard, Jean-Emmanuel Deschaud and FranÃ§ois Goulette
65	SnapNet_	0.591	0.886	3600.00	0.820	0.773	0.797	0.229	0.911	0.184	0.373	0.644
Unstructured point cloud semantic labeling using deep segmentation networks. A. Boulch, B. Le Saux and N. Audebert, Eurographics 3DOR 2017
66	CASIA_VSLab	0.720	0.902	243.06	0.793	0.829	0.913	0.536	0.888	0.454	0.633	0.718
Anonymous submission
67	TML-PCR	0.384	0.740	0.00	0.726	0.730	0.485	0.224	0.707	0.050	0.000	0.150
Mind the gap: modeling local and global context in (road) networks: Javier Montoya, Jan D. Wegner, Lubor Ladicky, Konrad Schindler. In: German Conference on Pattern Recognition (GCPR), MÃ¼nster, Germany, 2014
68	10GRUDeep3	0.468	0.790	10800.00	0.475	0.481	0.692	0.268	0.920	0.180	0.309	0.422
Anonymous submission
69	lixu_163_test	0.003	0.005	231.50	0.005	0.000	0.004	0.002	0.004	0.005	0.001	0.000
Anonymous submission
70	WYJ_JTP	0.002	0.006	1000.00	0.001	0.000	0.001	0.005	0.004	0.007	0.000	0.000
Anonymous submission
71	NLNN	0.203	0.539	1.00	0.000	0.000	0.864	0.251	0.506	0.000	0.000	0.000
Anonymous submission

@inproceedings{hackel2017isprs,
title={{SEMANTIC3D.NET: A new large-scale point cloud classification benchmark}},
author={Timo Hackel and N. Savinov and L. Ladicky and Jan D. Wegner and K. Schindler and M. Pollefeys},
booktitle={ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
year = {2017},
volume = {IV-1-W1},
pages = {91--98}
}

reduced-8 results

References